Filtering of Neural Signals for Mental Control of Robotic Prosthetic Devices

We discuss the problem of "decoding" intended hand motion from direct measurement of neurons in the motor cortex, for the purpose of driving a prosthetic device. By building probabilistic models and making use of nonlinear non-Gaussian filtering techniques, we are able to obtain estimates of intended hand motion, along with associated standard errors. We use a refinement of a previous state-of-the-art model, and demonstrate how the filtering approach works in analysis of multi-neuron recordings collected from a monkey carrying out a "center-out" task.